{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T13:52:00Z","timestamp":1768312320018,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T00:00:00Z","timestamp":1625702400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","award":["UIDB\/00319\/2020"],"award-info":[{"award-number":["UIDB\/00319\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to recognize audio, image, and time series predictions, has helped to solve various types of problems. This paper aims to introduce a new theory, Hierarchical Temporal Memory (HTM), that applies to stock market prediction. HTM is based on the biological functions of the brain as well as its learning mechanism. The results are of significant relevance and show a low percentage of errors in the predictions made over time. It can be noted that the learning curve of the algorithm is fast, identifying trends in the stock market for all seven data universes using the same network. Although the algorithm suffered at the time a pandemic was declared, it was able to adapt and return to good predictions. HTM proved to be a good continuous learning method for predicting time series datasets.<\/jats:p>","DOI":"10.3390\/electronics10141630","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T10:42:17Z","timestamp":1625740937000},"page":"1630","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Hierarchical Temporal Memory Theory Approach to Stock Market Time Series Forecasting"],"prefix":"10.3390","volume":"10","author":[{"given":"Regina","family":"Sousa","sequence":"first","affiliation":[{"name":"ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal"}]},{"given":"Tiago","family":"Lima","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal"}]},{"given":"Ant\u00f3nio","family":"Abelha","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4121-6169","authenticated-orcid":false,"given":"Jos\u00e9","family":"Machado","sequence":"additional","affiliation":[{"name":"ALGORITMI Research Center, School of Engineering, Gualtar Campus, University of Minho, 4710-057 Braga, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Neto, C., Brito, M., Peixoto, H., Lopes, V., Abelha, A., and Machado, J. (2020). Prediction of Length of Stay for Stroke Patients Using Artificial Neural Networks. World Conference on Information Systems and Technologies, Springer.","DOI":"10.1007\/978-3-030-45688-7_22"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.tics.2006.04.008","article-title":"Is neocortex essentially multisensory?","volume":"10","author":"Ghazanfar","year":"2006","journal-title":"Trends Cogn. Sci."},{"key":"ref_3","unstructured":"Purdy, S. (2016). Encoding data for HTM systems. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.1162\/NECO_a_00893","article-title":"Continuous Online Sequence Learning with an Unsupervised Neural Network Model","volume":"28","author":"Cui","year":"2016","journal-title":"Neural Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Maltoni, D. (2021, January 12). Pattern Recognition by Hierarchical Temporal Memory. Available online: http:\/\/dx.doi.org\/10.2139\/ssrn.3076121.","DOI":"10.2139\/ssrn.3076121"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-015-0313-4","article-title":"A Soft Computing Approach to Kidney Diseases Evaluation","volume":"39","author":"Neves","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s11036-018-1071-6","article-title":"A Deep-Big Data Approach to Health Care in the AI Age","volume":"23","author":"Neves","year":"2018","journal-title":"Mob. Netw. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.4236\/ojs.2016.66086","article-title":"Forecasting S&P 500 Stock Index Using Statistical Learning Models","volume":"6","author":"Liu","year":"2016","journal-title":"Open J. Stat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.eswa.2017.04.030","article-title":"Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies","volume":"83","author":"Chong","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"55392","DOI":"10.1109\/ACCESS.2018.2868970","article-title":"Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network","volume":"6","author":"Minh","year":"2018","journal-title":"IEEE Access."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nelson, D.M.Q., Pereira, A.C.M., and de Oliveira, R. (2017, January 14\u201319). Stock market\u2019s price movement prediction with LSTM neural networks. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102741","DOI":"10.1016\/j.dsp.2020.102741","article-title":"An improved deep learning model for predicting stock market price time series","volume":"102","author":"Liu","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kulkarni, D., Jadha, D., and Dhingra, D.D. (2020, January 14\u201317). Time Series and Data Analysis and for Stock and Market Prediction. Proceedings of the 3rd International Conference on Innovative Computing and Communication, Ho Chi Minh City, Vietnam.","DOI":"10.2139\/ssrn.3563111"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bao, W., Yue, J., and Rao, Y. (2017). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE., 12.","DOI":"10.1371\/journal.pone.0180944"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gabrielsson, P., K\u00f6nig, R., and Johansson, U. (2013). Evolving Hierarchical Temporal Memory-Based Trading Models. European Conference on the Applications of Evolutionary Computation, Springer.","DOI":"10.1007\/978-3-642-37192-9_22"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","article-title":"Unsupervised real-time anomaly detection for streaming data","volume":"262","author":"Ahmad","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.neucom.2017.08.026","article-title":"Hierarchical Temporal Memory method for time-series-based anomaly detection","volume":"273","author":"Wu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Anandharaj, A., and Sivakumar, P.B. (2019, January 12\u201314). Anomaly Detection in Time Series data using Hierarchical Temporal Memory Model. Proceedings of the 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India.","DOI":"10.1109\/ICECA.2019.8821966"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cui, Y., Surpur, C., Ahmad, S., and Hawkins, J. (2016, January 24\u201329). A comparative study of HTM and other neural network models for online sequence learning with streaming data. Proceedings of the 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, Canada.","DOI":"10.1109\/IJCNN.2016.7727380"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.neucom.2018.09.098","article-title":"Hierarchical temporal memory and recurrent neural networks for time series prediction: An empirical validation and reduction to multilayer perceptrons","volume":"396","author":"Struye","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-642-28670-4_2","article-title":"An Integrated Hierarchical Temporal Memory Network for Continuous Multi-Interval Prediction of Stock Price Trends","volume":"Volume 413","author":"Kang","year":"2012","journal-title":"Software and Network Engineering"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yilmazkuday, H. (2021, January 02). COVID-19 Effects on the S&P 500 Index. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3555433.","DOI":"10.2139\/ssrn.3555433"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104274","DOI":"10.1016\/j.jpubeco.2020.104274","article-title":"Economic Uncertainty Before and During the COVID-19 Pandemic","volume":"191","author":"Altig","year":"2020","journal-title":"J. Public Econ."},{"key":"ref_24","unstructured":"(2021, January 02). Stock Market News. Available online: https:\/\/www.marketwatch.com."},{"key":"ref_25","unstructured":"Hong, W.C., Li, M.W., and Fan, G.F. (2019). Short-Term Load Forecasting by Artificial Intelligent Technologies, MDPI-Multidisciplinary Digital Publishing Institute."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Klukas, M., Lewis, M., and Fiete, I. (2021, January 02). Efficient and Flexible Representation of Higher-Dimensional Cognitive Variables with Grid Cells. Available online: https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1007796.","DOI":"10.1371\/journal.pcbi.1007796"}],"container-title":["Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9292\/10\/14\/1630\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:27:42Z","timestamp":1760164062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9292\/10\/14\/1630"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,8]]},"references-count":26,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["electronics10141630"],"URL":"https:\/\/doi.org\/10.3390\/electronics10141630","relation":{},"ISSN":["2079-9292"],"issn-type":[{"value":"2079-9292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,8]]}}}